Dilated Volumetric Network: an Enhanced Fully Convolutional Network for Volumetric Prostate Segmentation from Magnetic Resonance Imaging

Early detection of prostate cancer is crucial for its successful treatment. However, it is not always an easy task because of the various image capturing configurations, like acquisition protocols, magnetic field strength, presence/absence of endorectal coil, and resolution. The major bottleneck in the process is the delineation of the prostate boundary for its localization, which is required for the detection of abnormalities and performing radiotherapy accurately. Phenomenal development in Artificial Intelligence and Deep Learning has been contributing significantly to medical diagnostics using Computer Vision and the self-learning capabilities of Deep Learning has been explored to present a viable solution to automate this repetitive task of prostate segmentation. The previous approaches of 2D segmentation do not capture volumetric information and are very time consuming too. Hence, we have developed a Deep Learning based automated solution called DV-Net (Dilated Volumetric Network) for volumetric segmentation of prostate cancer. The proposed method considers the full prostate volume in 3D and requires minimal post-processing, which makes it less dependent on the type of input. We also focus on increasing the receptive field of the network and use deep supervision for better segmentation accuracy. Owing to all these features, DV-Net has shown to outperform the accuracy of the baseline V-Net model on the Prostate MR Image Segmentation (PROMISE) data set.